TY - JOUR T1 - RF-Net 2: Fast Inference of Virus Reassortment and Hybridization Networks JF - bioRxiv DO - 10.1101/2021.05.05.442676 SP - 2021.05.05.442676 AU - Alexey Markin AU - Sanket Wagle AU - Tavis K. Anderson AU - Oliver Eulenstein Y1 - 2021/01/01 UR - http://biorxiv.org/content/early/2021/05/06/2021.05.05.442676.abstract N2 - Motivation A phylogenetic network is a powerful model to represent entangled evolutionary histories with both divergent (speciation) and convergent (e.g., hybridization, reassortment, recombination) evolution. The standard approach to inference of hybridization networks is to (i) reconstruct rooted gene trees and (ii) leverage gene tree discordance for network inference. Recently, we introduced a method called RF-Net for accurate inference of virus reassortment and hybridization networks from input gene trees in the presence of errors commonly found in phylogenetic trees. While RF-Net demonstrated the ability to accurately infer networks with up to four reticulations from erroneous input gene trees, its application was limited by the number of reticulations it could handle in a reasonable amount of time. This limitation is particularly restrictive in the inference of the evolutionary history of segmented RNA viruses such as influenza A virus (IAV), where reassortment is one of the major mechanisms shaping the evolution of these pathogens.Results Here we expand the functionality of RF-Net that makes it significantly more applicable in practice. Crucially, we introduce a fast extension to RF-Net, called Fast-RF-Net, that can handle large numbers of reticulations without sacrificing accuracy. Additionally, we develop automatic stopping criteria to select the appropriate number of reticulations heuristically and implement a feature for RF-Net to output error-corrected input gene trees. We then conduct a comprehensive study of the original method and its novel extensions and confirm their efficacy in practice using extensive simulation and empirical influenza A virus evolutionary analyses.Availability RF-Net 2 is available at https://github.com/flu-crew/rf-net-2.Competing Interest StatementThe authors have declared no competing interest. ER -